Calculate and interpret the results of Tukey

As explained in the first exercise and as seen in the video, anytime you engage in NHST, a type I error can occur. In a situation were you do multiple pairwise comparisons, the probability of type I errors in the process inflates substantially. Therefore, it is better to build in adjustments to take this into account. This is what Tukey tests and other post-hoc procedures do. They adjust the p-value to prevent inflation of the type I error rate.

In R you can use Tukey's procedure via the TukeyHSD() function.

The dataset wm is still loaded in your working environment.

Use an ANOVA analysis to compare IQ gain across all groups and save the model in the variable anova_wm.
Print the summary statistics of the model to the console. Recall that you already have done such an analysis in chapter 1, so you should be comfortable doing this.
Use TukeyHSD() to conduct to evaluate all pairwise comparisons. Store the result in tukey.
Call plot() on the result from Tukey's procedure to plot confidence intervals for the mean differences of the different pairwise comparisons.

Take Hint (-30 XP)

Exercise

Calculate and interpret the results of Tukey

Instructions